www.cmu.edu Abstract: The Open Learning Initiative (OLI) is an open educational resources project at Carnegie Mellon University that began in 2002 with a grant from The William and Flora Hewlett Foundation. OLI creates web-based courses that are designed so that students can learn effectively without an instructor. In addition, the courses are often used by instructors to support and complement face-to-face classroom instruction. Our evaluation efforts have investigated OLI courses ’ effectiveness in both of these instructional modes – stand-alone and hybrid. This report documents several learning effectiveness studies that were focused on the OLI-Statistics course and conducted during Fall 2005, Spring 2006, and Spring 2007. During the Fall 2005 and Spring 2006 studies, we collected empirical data about the instructional effectiveness of the OLI-Statistics course in stand-alone mode, as compared to traditional instruction. In both of these studies, in-class exam scores showed no significant difference between students in the stand-alone OLI-Statistics course and students in the traditional instructor-led course. In contrast, during the Spring 2007 study, we explored an accelerated learning hypothesis, namely, that learners using the OLI course in hybrid mode will learn the same amount of material in a significantly shorter period of time with equal learning gains, as compared to students in traditional instruction. In this study, results showed that OLI-Statistics students learned a full semester’s worth of material in half as much time and performed as well or better than students learning from traditional instruction over a full semester.

Abstract. This paper describes the use of independent open learner models that prompt spontaneous collaboration amongst students, and suggest that these results could be built upon in systems that offer more explicit support for collaboration and student interaction. We focus in particular on the promotion of learner discussion around their misconceptions. 1

The multidisciplinary study of complex systems in the physical and social sciences over the past quarter of a century has led to the articulation of important new conceptual perspectives and methodologies that are of value both to researchers in these fields as well as to professionals, policymakers, and citizens who must deal with challenging social and global problems in the 21st century. The main goals of this article are to (a) argue for the importance of learning these ideas at the precollege and college levels; (b) discuss the significant challenges inherent in learning complex systems knowledge from the standpoint of learning sciences theory and research; (c) discuss the “learnability issue ” of complex systems conceptual perspectives and review a body of literature that has been exploring how learning sciences pedagogical approaches can lead to student learning of important dimensions of complex systems knowledge; (d) argue that the cognitive and sociocultural factors related to learning complex systems knowledge are relevant and challenging areas for learning sciences research; and (e) consider ways that concepts and methodologies from the study of complex systems raise important issues of theoretical and methodological centrality in the field of the learning sciences itself.

This paper details the implementation of an agent-based educational gaming environment utilizing the REAL cognitive framework (Reflective Agent Learning Environment). REAL provides an interactive learning environment that allows students to 1) construct their imaginary world; 2) reflect upon the quality of their understanding; 3) test it out in the dynamically generated simulation games. The REAL framework stresses reflection as the critical component of thinking processes. The reflective agent’s action is monitored by an expert agent, a pedagogical agent, and a communication agent. With a rule-based reasoning engine and game engine embedded, REAL makes it easier for developers to model domain knowledge and develop simulation games that are otherwise time-consuming. Our studies show that this kind of learning environment engages students in learning and encourages collaboration among researchers in different areas. 1.

Furthering our understanding of what it means to make sense of complex systems is becoming a pressing imperative, as educational systems begin incorporating such constructs into standard curricula (Jacobson, 2006; Wilensky, 1999; Levy, Novak & Wilensky, 2006). Work to date on the topic has focused on understanding of the system’s structural aspects (Hmelo-Silver & Pfeffer, 2004) or the underlying epistemologies and ontologies (Wilensky & Resnick, 1999; Jacobson, 2001; Chi, 2005). This work builds upon previous work, delineating how people reason about complex systems. We propose a framework, Actions Across Levels (AAL), for understanding and investigating how people reason about complex systems. This framework consists of two dimensions: description levels and mental actions undertaken while interpreting systems (see Figure 1). The notion of levels is a central component in agent-based approaches, specifying both individual agents and the overall system’s emergent and aggregate behavior (Bar-Yam, 1997). Thus, one dimension in the AAL framework is the description level: agent (individuals), aggregate (system) or a mélange of the two (AA, agent-aggregate 1 complementarity, when both description levels are incorporated into a single explanation;

Since the recent inclusion of mathematical reasoning as an aspect of student’s mathematical competence, the development of students ’ mathematical reasoning has become one of the main issues in mathematics education (NCTM, 1989, 1991, 2000; Stiff & Curcio, 1999). From a cognitive standpoint, students ’ processes of reasoning can be influenced by their embodied

We report on a study with 65 middle-school students who learned about the concept of diffusion through observation. We manipulated two factors: the number of observers, solo vs. dyad, and the type of video students observed, tutorial dialogue vs. monologue. Our findings show that dyad observers learn significantly better than solo observers, and that for certain types of questions, observing dialogue results in better learning gains, as compared to observing monologue.

There has been a body of emerging research describing students ’ understanding of complex systems. This research has primarily studied students understanding of complex phenomena in science. However, complex phenomena are also pervasive in everyday life. Children observe and participate in them daily. How do they reason about such ordinary complex phenomena? In this study, we investigate students’ reasoning about everyday complex phenomena. We report on interviews and a classroom participatory simulation with ten sixth-grade students about ordinary events that could be construed as emergent, such as social situations in which the social pattern emerges from the participating students ’ individual actions. We have observed a widespread student-initiated strategy for making sense of complex phenomena. We call this strategy “mid level construction, ” the formation of small groups of individuals. Students form these mid-level groups either by aggregating individuals or by subdividing the whole group. We describe and characterize this mid-level strategy and relate it to the students ’ expressed understanding of “complex systems” principles. The results are discussed with respect to (a) students ’ strengths in understanding everyday complex social systems; (b) the utility of mid-level groups in forming an understanding of complex systems; (c) agent-based and aggregate forms of reasoning about complex systems.

Abstract This study explores young children’s ability to construct and explain adaptive behaviors of a behaving artifact, an autonomous mobile robot with sensors. A central component of the behavior construction environment is the RoboGan software that supports children’s construction of spatiotemporal events with an a-temporal rule structure. Six kindergarten children participated in the study, three girls and three boys. Activities and interviews were conducted individually along five sessions that included increasingly complex construction tasks. It was found that all of the children succeeded in constructing most such behaviors, debugging their constructions in a relatively small number of cycles. An adult’s assistance in noticing relevant features of the problem was necessary for the more complex tasks that involved four complementary rules. The spatial scaffolding afforded by the RoboGan interface was well used by the children, as they consistently used partial backtracking strategies to improve their constructions, and employed modular construction strategies in the more complex tasks. The children’s explanations following their construction usually capped at one rule, or two condition-action couples, one rule short of their final constructions. With respect to tasks that involved describing a demonstrated

While many computer science students plan to pursue careers as software engineers, research shows that most traditional undergraduate CS programs fail to prepare students for the realities of programming in industry. Many misconceptions that are interfering with the transition to industry are belief-oriented, not skill-oriented, in nature, so traditional misconception assessments will not yield a deep understanding of them. In this paper we present a novel methodology that shows interactions among the misconceptions based on a forced choice paradigm and reveals the relative strength of the misconceptions. By analyzing students ’ repeated responses and response times, we construct a model of participants ’ misconceptions. We used this methodology to assess CS undergraduates at Carnegie Mellon University and compared their results to those from industry practitioners at several highly regarded companies. The results show that the students have misconceptions about process and teamwork. Surprisingly, we found that several misconceptions are correlated with elective courses that we expected to weaken misconceptions about software engineering but instead appeared to strengthen them.